Comp541 Deep Learning lecture videos on YouTube

The whole playlist.
Guest lecture by Alan Edelman: Why Julia?
Introduction to Neural Networks
Introduction to Julia
Backpropagation
Softmax Classification
Multi Layer Perceptrons
Convolutional Networks I
Convolutional Networks II
Convolutional Networks III
Recurrent Networks I
Recurrent Networks II
Methodology
Visualization
Optimization
Generalization
Generative Models
Reinforcement Learning: Intro
RL: Markov Decision Processes
RL: Dynamic Programming
RL: Model-free Methods
RL: Value Function Approximation and Policy Gradient Methods


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Knet-the-Julia-dope: An interactive book on deep learning.

Written by Manuel Antonio Morales (@moralesq). This repo is the Julia translation of the mxnet-the-straight-dope repo, a collection of notebooks designed to teach deep learning, MXNet, and the gluon interface. This project grew out of the MIT course 6.338 Modern Numerical Computing with Julia taught by professor Alan Edelman. Our main objectives are:

• Introduce the Julia language and its main packages in the context of deep learning
  
• Introduce Julia's package Knet: an alternative/complementary option to MXNet
  
• Leverage the strengths of Jupyter notebooks to present prose, graphics, equations, and code together in one place

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Wasserstein GAN: a Julia/Knet implementation

Written by Cem Eteke (@ceteke). This repository contains implementation of WGAN and DCGAN in Julia using Knet. Here is a detailed report about WGAN.
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Relational networks: a Julia/Knet implementation

Written by Erenay Dayanık (@ereday). Knet implementation of "A simple neural network module for relational reasoning" by Santoro et al. (2017). (Relational Networks, arXiv:1706.01427, blog post)
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Fast multidimensional reduction and broadcast operations on GPU for machine learning

Doğa Dikbayır, Enis Berk Çoban, İlker Kesen, Deniz Yuret, Didem Unat. Concurrency and Computation: Practice and Experience. 2018. (PDF). Abstract: Reduction and broadcast operations are commonly used in machine learning algorithms for different purposes. They widely appear in the calculation of the gradient values of a loss function, which are one of the core structures of neural networks. Both operations are implemented naively in many libraries usually for scalar reduction or broadcast; however, to our knowledge, there are no optimized multidimensional implementations available. This fact limits the performance of machine learning models requiring these operations to be performed on tensors. In this work, we address the problem and propose two new strategies that extend the existing implementations to perform on tensors. We introduce formal definitions of both operations using tensor notations, investigate their mathematical properties, and exploit these properties to provide an efficient solution for each. We implement our parallel strategies and test them on a CUDA enabled Tesla K40 m GPU accelerator. Our performant implementations achieve up to 75% of the peak device memory bandwidth on different tensor sizes and dimensions. Significant speedups against the implementations available in the Knet Deep Learning framework are also achieved for both operations.
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